Fractional Flow Reserve

2. PURPOSE:
• To compare the diagnostic accuracy of different
computed tomographic (CT) fractional flow
reserve (FFR) algorithms for vessels with
intermediate stenosis (25%-69%).
• To date, three CT FFR technologies have been
validated against conventional FFR.
1. Huo-Kassab model
2. Murray law model
3. Transluminal Attenuation Gradient (TAG)
method.

3. Material and Methods:
• This cross-sectional study applied a four-step CT FFR
method in 61 patients (Age range: 29–89 years) with
a lesion of intermediate-diameter stenosis (25%–69%)
at CT angiography who underwent FFR measurement
within 90 days.
• The per-lesion diagnostic performance of CT FFR was
tested for three different approaches to estimate blood
flow distribution for CT FFR calculation.
• The first two, the Murray law and the Huo-Kassab rule,
used coronary anatomy; the third Transluminal
Attenuation Gradient (TAG) used contrast material
opacification gradients.

4. Material and Method:
• CT FFR algorithms and CT angiography percentage
diameter stenosis (DS) measurements were compared
by using the area under the receiver operating
characteristic curve (ROC curve) to detect
FFRs of 0.8 or lower.

5. • In general, visual stenosis grading at coronary CT
angiography overestimates lesion severity compared with
quantitative coronary angiography .
• Thus, coronary CT angiography shows poor predictive
value in identifying hemodynamically significant
coronary stenosis .

6. FFR cutoff 0.80 or less, why?
• It is well accepted that lesions with FFR values of less
than 0.75 are myocardial ischemia inducing, whereas
FFR values of greater than 0.80 rule out lesion-specific
ischemia, with a negative predictive value of greater than
95%. Thus, a “gray zone” exists for FFR values between
0.75 and 0.80.
• In the DEFER trial, an FFR threshold of 0.75 or less was
used to indicate lesion specific ischemia, whereas in the
FAME I and FAME II multicenter trials, lesion specific
ischemia was defined as FFR values of 0.80 or less.
• This cutoff value of 0.80 or less is the number
recommended in current guidelines.

7. CCA and FFR measurement:-
• CCA and FFR were performed according to the
institutional clinical protocol with a femoral or radial
approach.
• FFR was measured by using a pressure wire advanced
past the stenosis after intracoronary injection of
papaverine hydrochloride (left coronary artery: 12 mg;
right coronary artery: 8 mg).
• An FFR of 0.80 or less was considered hemodynamically
significant.
• CCA images were retrospectively analyzed by using
quantitative coronary angiography software (QAngio;
MEDIS) to determine percentage DS of the target lesion.

8. CT angiography:-
Patients were imaged with a 320 x 0.5-mm detector row
CT scanner. The average heart rate during CT angiography
was 64 beats per minute (range, 46–88 beats per minute).
Patients with a heart rate of 65 or more beats per minute
received oral metoprolol 3 hours before Imaging or
0.125 mg/Kg body weight intravenous landiolol (Onoact)
immediately prior to imaging.
Patients with systolic blood pressure of 110 mm Hg or greater
received 0.3 mg sublingual nitroglycerin.

9. • Computed tomographic (CT) FFR technology
noninvasively estimates FFR from CT angiography
data by using computational fluid dynamics (CFDs) for
the pressure drop across a coronary artery .
It involves the following four steps:
a) Coronary segmentation.
b) Hyperemic blood flow demand calculations.
c) Blood flow distribution calculations, and
d) Computational Fluid Dynamics (CFD) simulation
of the blood flow

11. 1. Coronary segmentation:
• Coronary segmentation was performed by using commercial
segmentation software (Toshiba Cardiac Analysis Package,
Toshiba Medical Systems).
• Segmented lumen contours were output by the segmentation
software at 0.5-mm intervals along the vessel centerlines
to reflect the largest voxel dimension of the CT angiography
data and used for further CFD analysis.

12. 2. Hyperemic blood flow demand calculations:-
• Estimation of total blood flow through the coronary
tree at maximum hyperemia.
• Total resting myocardial blood flow was calculated
assuming myocardium requires 0.8 mL/min/g of
blood at rest .
• The left ventricular myocardium, assumed to represent
2/3rd of total myocardial mass , was automatically
segmented from CT angiography images in Vitrea 6.7
postprocessing software.
• It is assumed that epicardial coronary arteries
presented negligible resistance to flow and that total
distal resistance was reduced to one-quarter its resting-
state value.

13. 3. Blood flow distribution calculations to each branch:-
• “Conservation of flow” was used to calculate the
distribution of flow to each coronary branch.
Specifically, flow arriving at a bifurcation was
conserved in the two daughter branches.
• Three models were used to determine the relative flow
to each branch.
1. Huo-Kassab model
2. Murray law model
3. Coronary contrast opacification gradient
(Transluminal Attenuation Gradient (TAG)

14. • The first two assumed that flow was proportional to the
“coronary diameter” to one of two powers, either the third
power (Q~D3, the Murray law) or the seven-thirds power
(Q~D7/3, the Huo-Kassab model).
• To apply these methods, the diameter of each branch was
measured immediately after, or at the first non diseased
location after, the bifurcation in cross-sectional images,
orthogonal to the vessel centerline.
• The third model used the coronary contrast opacification
gradient (transluminal attenuation gradient [TAG]) to
estimate flow. TAG was measured by using previously
validated software .

15. 4. CFD simulations:-
• A commercial CFD software suite (ANSYS, Canonsburg, Pa) was
used and was applied identically for each of the three
blood flow distribution models to calculate the pressure.
CT FFR calculation:-
The CT FFR algorithm steps described above were
performed on standard desktop workstations, with blinding
to all patient characteristics, including CT angiography,
CCA, and FFR findings.
• To calculate CT FFR, the pressure solved by CFD was
interrogated at the location matching the invasive FFR
measurement.

19. RESULTS:-

20. • The average target lesion percentage DS was 47.2% +
8.6 (range, 30.9%–64.8%) at quantitative coronary CT
angiography.
• Twenty-five lesions (41%) had an FFR of 0.8 or less.
• The AUC of CT FFR determination by using contrast material
gradients TAG(AUC = 0.953) was significantly higher than that
of the Huo-Kassab (AUC = 0.882) and Murray law models
(AUC = 0.871).

22. • The AUC of CT FFR determination by using contrast material
gradients TAG(AUC = 0.953) was significantly higher than that of the
Huo-Kassab (AUC = 0.882) and Murray law models (AUC = 0.871).
• Correlation of CT FFR with FFR was highest for gradients (TAG),
followed by the Huo-Kassab rule and Murray law models.

24. • Min et al (2012) first reported an accuracy of 86% for
the detection of FFR of 0.8 or less in lesions with 40%–
70% angiographic stenosis.
• Nakazato et al (2013) using same technology found a
lower accuracy of 71% for lesions with 30%–69%
stenosis at CT angiography, but a nonetheless improved
discrimination of hemodynamically significant disease
compared with CT angiography interpretation alone.
• Coenen et al (2015) reported a similar accuracy of 71.5%
in lesions with 25%–69% stenosis at CT angiography
using a different CT FFR algorithm (cFFR, Siemens
Healthcare).

25. • The most recent NXT trial of the initial FFRct technology
improved on these results, realizing an accuracy of 80% in
vessels with 30%–70% stenosis at CT angiography.
• In the present study, two models using coronary diameter
technology (similar to the initial CT FFR technology)
had accuracies for the detection of FFR of 0.8 or less
were comparable to that in the study by Min et al
(Accuracy 85%vs86%).
• Coronary contrast opacification gradients (TAG) method
have the higher overall accuracy (92%) for the detection
of FFR of 0.8 or less .

27. • Lesions of intermediate stenosis severity pose a challenge for
management, as they can cause ischemia despite not appearing
angiographically severe.
• A study (Curzen et al) of patients with chest pain in stable condition
referred for coronary angiography reported rates of invasive FFR of
0.8 or less of 13% for lesions of less than 30% DS,
33% for lesions of 31%–50% DS, and
33% for lesions of 50%–70% DS.
• CT FFR algorithms can estimate FFR from CT angiography data
toward reducing unnecessary invasive angiography referrals .
• The accuracy of several such algorithms has generally been
favorable.

28. Conclusion:
• Correlation of CT FFR and invasive FFR was good for all
three CT FFR models. It was highest for TAG, followed
by the Huo-Kassab rule and Murray law model.
• Discrimination of hemodynamically significant disease
was improved with all three CT FFR algorithms compared
with DS of 50% or greater at CT angiography.
• CT FFR can reducing the unnecessary invasive
angiography referrals. The accuracy of several such
algorithms has generally been favorable with stenosis of
intermediate severity.

29. Thank you